Data is the backbone of any analysis, decision-making, or reporting process.
However, data is often prone to errors, inconsistencies, and duplications. Cleaning and validating data in a spreadsheet is a crucial step to ensure accurate and reliable results.
In this latest Grey Data Blog post, we’ll walk you through the latest and greatest methodologies to check and cleanse data in a MS Excel spreadsheet.
- REVIEW THE DATA SET: Before diving into data cleansing, carefully review the entire spreadsheet to identify any obvious errors or inconsistencies. Look out for misspellings, incorrect data formats, missing values, and inconsistent data entries. Understanding the scope of the data issues will help you plan an effective data cleansing strategy.
- CREATE A BACKUP: Data cleansing involves making changes to your dataset, and sometimes mistakes can happen. Before you begin the process, make a backup of your original data. This way, you can always revert to the original version if anything goes wrong during the cleansing process.
- ASCII IS YOUR FRIEND: Do not be scared to use Notepad, Notepad++, or something similar to remove any styling. Save and import as deemed fit.
- DEDUPE, CHECK, DEDUPE: Duplicates can skew your analysis and lead to inaccurate results. Use spreadsheet functions or built-in tools to identify and remove duplicates from your dataset. Be cautious when removing duplicates and make sure you understand the criteria for considering entries as duplicates.
- HANDLE THE MISSING DATA: Missing data is a common issue in spreadsheets. Decide how to handle missing data points based on your analysis requirements. You can choose to fill in missing data with averages, use interpolation, or remove rows with missing values if they don’t significantly impact the overall analysis.
- INITIAL VALIDATION: Implement data validation rules to restrict the type and range of data entered in specific cells. Data validation helps maintain data integrity and reduces the likelihood of errors. For example, you can set a rule to allow only numerical data in a certain column or limit dates to a specific range.
- STANDARDISE FORMATS: Inconsistent data formats can cause confusion and hinder analysis. Standardise formats for dates, numbers, and other categorical data to ensure uniformity. This may involve converting text to uppercase or lowercase, using date formats consistently, and aligning number formats.
- USE FORMULAS FOR CONSISTENCY: Formulas in spreadsheets can help ensure data consistency. Use formulas to calculate values automatically, rather than manually entering them. This reduces the risk of human errors and maintains the integrity of your data.
- CHECK FOR OUTLIERS: Outliers are data points significantly different from others in the dataset. Outliers can skew your analysis and conclusions. Use statistical methods to identify and evaluate outliers. You can choose to remove outliers if they are the result of data entry errors or perform sensitivity analyses to see how they affect your results.
- VERIFY EVERYTHING: If your data includes relationships between different variables, verify that these relationships are consistent and accurate. For example, if you have a column for ‘Age’ and another for ‘Date of Birth’, it goes without saying that you should check to be sure the age is calculated correctly based on the birthdate.
- TEST PROCEDURES: After implementing various data cleansing techniques, perform test runs to validate the effectiveness of your procedures. Remember to check a sample of your data to see if the cleansing has been carried out correctly. Maybe worth repeating this throughout the worksheet, if the data set is large. If issues persist, refine your data cleansing approach and repeat the process until you are satisfied with the result. A bit of time and care taken here will greatly improve the end result.
- DOCUMENT THE CHANGES AND THE METHODOLOGY: Document all the changes you make to the data and the methodologies used for data cleansing. This documentation will be valuable for future reference and for collaborating with others in your team or organisation.
- VALIDATE THE DATA: Data is rarely static; and it evolves over time. Ensure that your spreadsheet data remains accurate by regularly updating and validating it. Set up a process for ongoing data maintenance and cleansing to avoid accumulation of errors and inconsistencies.
A business is often only as good as its data. Data cleansing is a fundamental step in the data analysis process. By following the key strategies we’ve included here to check and cleanse the data in a spreadsheet, you can ensure that your data is accurate, consistent, and as reliable as possible.
Remember, clean and reliable data forms the foundation for informed (good) decision-making, the most accurate analysis, and most useful insights, allowing you to drive success in your endeavours.